Abstract
Background: The poor prognosis of patients with acute ischemic stroke (AIS) after bridging therapy (BT) imposes a heavy burden on their families. This study decided to investigate the risk factors for poor prognosis and establish a predictive model.Objective: To explore the risk factors of poor prognosis in patients with AIS after BT.Methods: The study included AIS patients treated with BT (intravenous thrombolysis with alteplase prior to endovascular thrombectomy) from January 2020 to December 2023 in the Hangzhou First People's Hospital. Modified Rankin scale (mRS)was used to assess the patient’s prognosis after 3 months, and these patients were divided into the poor prognosis group (mRS > 2) and good prognosis group (mRS ≤ 2) according to the mRS.The patients' history of chronic diseases and the laboratory testing data were recorded. SPSS 25 was used for statistical analysis.Receiver operating characteristics (ROC) curves and logistic regression analysis were used to explore associated factors of AIS treated with BT.Results: We studied 120 AIS patients treated with BT.The poor prognosis group included 65 cases and good prognosis group included 55 cases.In the poor prognosis group, the patients with higher proportion of stroke-associated pneumonia (SAP), Symptomatic intracranial hemorrhage(sICH) and intracranial hemorrhage (ICH), and with higher NIHSS score at admission were older, concomitantly, the fasting plasma glucose (FBG) was significantly higher than those of the good prognosis group (P < 0.05). Multivariate logistic regression analysis showed SAP and NIHSS score were independent risk factors for poor prognosis of patients with AIS after BT (P < 0.05).The ROC analysis showed that the area under curve (AUC) of SAP was 0.717 (95% CI = 0.622–0.811), for the NIHSS score, the AUC was 0.716 (95% CI = 0.624–0.807), and the optimal cutoff threshold, sensitivity, and specificity were 15.4, 0.754, 0.564 respectively.When SAP combined with NIHSS score,we created a 2-item prediction model.In this model, the AUC increased to 0.809 (95% CI = 0.732–0.886), and the optimal cut-off, sensitivity, and specificity were 0.522,0.831, 0.691 respectively.Conclusion: Age, FBG, SAP, sICH ,ICH, and NIHSS score at admission were associated with poor prognosis of AIS patients after BT, while SAP and NIHSS score were independent risk factors for poor prognosis. The NIHSS score plus the SAP had a high diagnostic performance and predictive value for poor prognosis in patients with AIS treated with BT.